| orf-package {orf} | R Documentation |
orf: Ordered Random Forests
Description
An implementation of the Ordered Forest estimator as developed
in Lechner & Okasa (2019). The Ordered Forest flexibly
estimates the conditional probabilities of models with ordered
categorical outcomes (so-called ordered choice models).
Additionally to common machine learning algorithms the orf
package provides functions for estimating marginal effects as well
as statistical inference thereof and thus provides similar output
as in standard econometric models for ordered choice. The core
forest algorithm relies on the fast C++ forest implementation
from the ranger package (Wright & Ziegler, 2017).
Author(s)
Gabriel Okasa, Michael Lechner
References
Lechner, M., & Okasa, G. (2019). Random Forest Estimation of the Ordered Choice Model. arXiv preprint arXiv:1907.02436. https://arxiv.org/abs/1907.02436
Goller, D., Knaus, M. C., Lechner, M., & Okasa, G. (2021). Predicting Match Outcomes in Football by an Ordered Forest Estimator. A Modern Guide to Sports Economics. Edward Elgar Publishing, 335-355. doi:10.4337/9781789906530.00026
Wright, M. N. & Ziegler, A. (2017). ranger: A fast implementation of random forests for high dimensional data in C++ and R. J Stat Softw 77:1-17. doi:10.18637/jss.v077.i01.
Examples
## Ordered Forest
require(orf)
# load example data
data(odata)
# specify response and covariates
Y <- as.numeric(odata[, 1])
X <- as.matrix(odata[, -1])
# estimate Ordered Forest with default settings
orf_fit <- orf(X, Y)
# print output of the orf estimation
print(orf_fit)
# show summary of the orf estimation
summary(orf_fit)
# plot the estimated probability distributions
plot(orf_fit)
# predict with the estimated orf
predict(orf_fit)
# estimate marginal effects of the orf
margins(orf_fit)